Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient' motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)-based BCI was proposed. The visual stimuli of four types of hand movements were designed to simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital region and to activate the sensorimotor region. Task-related component analysis was performed to identify the SSMVEP. Results showed that the amplitude of the induced frequency in the SSMVEP had a negative relationship with the stimulus frequency. The classification accuracy in the four-class scenario reached 72.81 ± 13.55% within 2.5s. Importantly, the AO-based closed-loop BCI, which provided visual feedback based on the SSMVEP, could enhance ERD compared with AO-alone. The increased attentiveness might be one key factor for the enhancement of the ERD in the designed AO-based BCI. In summary, the proposed AO-based BCI provides a new insight for BCI-based rehabilitation.